Diversity Maintenance Strategy Based on Global Crowding

  • Authors:

  • Qiong Chen;Shengwu Xiong;Hongbing Liu

  • Affiliations:

  • School of Computer Science and Technology, Wuhan University of Technology, Wuhan, P. R. China 430070;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, P. R. China 430070;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, P. R. China 430070

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
  • Year:
  • 2009

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Abstract

In the design of multi-objective evolutionary algorithm, the diversity maintenance is essential to access the convergence of multi-objective optimization solutions. This paper presents a new diversity maintenance strategy based on global crowding, which is addressed for pruning non-dominated solutions as well as preserving a wide-spread distributed solution set and maintaining population diversity. Later on, inspired by the conception of entropy in information theory, the entropy metrics is defined and applied to assess the proposed strategy. Two-dimensional and multi-dimensional numerical experiment results demonstrate that the proposed strategy shows better performance in the entropy reduction and losses of uniform distribution than traditional diversity maintenance strategies.